U Mh@s<dZddlZddlmZmZddlZddlmZddlmZm Z m Z ddl m Z m Z mZddd gZejejd d Zed e d dd d dde jejeeejejeedddZede jddd ZddZede d dddd ddde jde jejeeeeeeejeeeeeejd ddZdS) aThis file exports ONNX ops for opset 17. Note [ONNX Operators that are added/updated in opset 17] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-17-of-the-default-onnx-operator-set New operators: BlackmanWindow DFT HammingWindow HannWindow LayerNormalization MelWeightMatrix STFT SequenceMap N)OptionalSequence)_C) _type_utilserrorssymbolic_helper) _beartype jit_utils registration layer_normstftquantized_layer_norm)Zopsetzaten::layer_normvisfnone)ginputnormalized_shapeweightbiaseps cudnn_enablec Cst| }tj|tjj}|} t|rJtj || d} |j d| d}t|rptj || d} |j d| d}|j d|||||dS)NdtypeConstantZvalue_tZLayerNormalization)Z epsilon_faxis_i) lenr JitScalarType from_valueFLOATrr_is_nonetorchonesopzeros) rrrrrrrZaxisZ scalar_typerZ weight_valueZ bias_valuer(M/var/www/html/venv/lib/python3.8/site-packages/torch/onnx/symbolic_opset17.pyr #s(   zquantized::layer_norm)rc Cs8t||\}}}}t||||||d} t|| ||S)NF)rZdequantize_helperr Zquantize_helper) rxrrrrZop_scaleZ op_zero_point_outputr(r(r)r Gs cCs ||d}|||}||fS)zuHelper function to compute the sizes of the edges (left and right) of a given window centered within an FFT size.r()n_fftZ window_sizeleftrightr(r(r)_compute_edge_sizesYs  r1z aten::stftibFT) rrr. hop_length win_lengthwindow normalizedonesidedreturn_complexreturnc  Cs|rtjd|d|dk r|n|d} |jdtj| tjdd} |jdtj|tjdd} |} t| } | dkr|d | |jdtjd gtjdd} n| d krtjd | d |dtj|d d}|dk rH|r|n|}||kst d|df||krHt ||\}}|jdt |d}|jdt |d}|jd|||d d}t |r|r||krtjd|d|d|dt ||\}}t t |t|t |f}n t|}|jd |kst |jd|d}|jd|tj| d}|jd| | || |dks|rdnd d}|jd|d d ddgd}| dkrl|d||jdtjd gtjdd}|rttj|| d}|d||jd|d}|S)aAssociates `torch.stft` with the `STFT` ONNX operator. Note that torch.stft calls _VF.stft, without centering or padding options. Hence, this function does not contain these two arguments. See torch.stft source code for more info. Args: g: Graph to write the ONNX representation into input: Input tensor for the transformation n_fft: FFT size hop_length: Size of the hop. Defaults to `floot(n_fft // 4)` win_length: Size of the analysis window. Defaults to `n_fft` window: Analysis window. Defaults to a window of all ones normalized: Whether to return a normalized STFT onesided: Whether to return only half (+1) of the results, given the symmetry of the STFT return_complex: Whether to return the complex value (Note: Must be `False` or `None`) Returns: op: Operator for torch.stft associated with STFT (ONNX) z-STFT does not currently support complex types)msgvalueNrrrZ Unsqueezerr-zcSTFT can only take inputs of 1 [signal] or 2 [batch, signal] dimensions. Current rank of signal is z, please reduce it.)dimzuAnalysis window size must equal `win_length` or `n_fft`. Please, set `win_length` or `n_fft` to match `window` size ()ZConcat)rzWThe analysis window can't be longer than the size of the FFT. Please set `win_length` (z) to `n_fft` (z ) or less.ZCast)Zto_iZSTFT)Z onesided_iZ Transpose)Zperm_iZSqueezeDiv)rZSymbolicValueErrorr&r$ZtensorZint64rZ_get_tensor_rankZ_get_tensor_dim_sizeAssertionErrorr1r'r#Zhstackr%shaperr r!Z onnx_typesqrttyper)rrr.r4r5r6r7r8r9Zframe_step_valueZframe_step_constZframe_length_constsignalZ signal_rankZn_winZwin_length_defaultr/r0Zleft_winZ right_winZ torch_windowresultZ sqrt_nfftr(r(r)r as$            )NNNFTF)__doc__ functoolstypingrrr$rZ torch.onnxrrrZtorch.onnx._internalrr r __all__partialZ onnx_symbolicZ_onnx_symbolic parse_argsZ GraphContextValueintfloatboolr r r1Zbeartyper r(r(r(r)sV  "